class BiLSTM_CRF(nn.Module):
def __init__(self, data):
super(BiLSTM_CRF, self).__init__()
print "build batched lstmcrf..."
self.gpu = data.HP_gpu
label_size = data.label_alphabet_size
data.label_alphabet_size += 2
self.lstm = BiLSTM(data)
self.crf = CRF(label_size, self.gpu)
def neg_log_likelihood_loss(self, gaz_list, word_inputs, biword_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover, batch_label, mask):
outs = self.lstm.get_output_score(gaz_list, word_inputs, biword_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover)
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
total_loss = self.crf.neg_log_likelihood_loss(outs, mask, batch_label)
scores, tag_seq = self.crf._viterbi_decode(outs, mask)
return total_loss, tag_seq
def forward(self, gaz_list, word_inputs, biword_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover, mask):
outs = self.lstm.get_output_score(gaz_list, word_inputs, biword_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover)
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
scores, tag_seq = self.crf._viterbi_decode(outs, mask)
return tag_seq
def get_lstm_features(self, gaz_list, word_inputs, biword_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover):
return self.lstm.get_lstm_features(gaz_list, word_inputs, biword_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover)
class BiLSTM_CRF(nn.Module):
def __init__(self, data):
super(BiLSTM_CRF, self).__init__()
print "build batched lstmcrf..."
self.gpu = data.HP_gpu
self.average_batch = data.HP_average_batch_loss
## add two more label for downlayer lstm, use original label size for CRF
label_size = data.label_alphabet_size
data.label_alphabet_size += 2
self.lstm = BiLSTM(data)
self.crf = CRF(label_size, self.gpu)
def neg_log_likelihood_loss(self, word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover, batch_label, mask):
outs = self.lstm.get_output_score(word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover)
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
total_loss = self.crf.neg_log_likelihood_loss(outs, mask, batch_label)
scores, tag_seq = self.crf._viterbi_decode(outs, mask)
if self.average_batch:
total_loss = total_loss / batch_size
return total_loss, tag_seq
def forward(self, word_inputs, word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover, mask):
outs = self.lstm.get_output_score(word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover)
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
scores, tag_seq = self.crf._viterbi_decode(outs, mask)
return tag_seq
def get_lstm_features(self, word_inputs, word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover):
return self.lstm.get_lstm_features(word_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover)